1. Field of the Invention
The present invention relates to a tenprint card selector which is used for a fingerprint matching device or the like for matching fingerprints with reference to tenprint cards showing fingerprints and selects a tenprint card corresponding to a sample from a plurality of tenprint card files based on a prescribed standard, and a tenprint card preselector which preselects tenprint cards to be checked by means of the tenprint card selector.
2. Description of the Related Art
When a fingerprint matching device which checks a fingerprint with reference to tenprint cards each having the record of applied fingerprints of ten fingers in a prescribed format is used to check a fingerprint, a tenprint card (hereinafter referred to as the search card) which shows newly collected fingerprint images is checked against the tenprint cards (hereinafter referred to as the file cards) which have the images of fingerprints collected previously in the above-described format and have been registered on a database in order to judge whether the file cards in a file include a file card which has the fingerprint images identical with those shown on the search card. The fingerprint matching device judges identity of the fingerprint images of corresponding fingers by matching the search card against all file cards in the files. Various types of methods have been proposed to judge identity of a given fingerprint image with one kept on record. The technology disclosed in Japanese Unexamined Patent Publication (Kokai) No. Showa 60-134386 "Fingerprint Checking Method" is one example of such fingerprint matching technologies.
But, the above matching method, which checks identity of the fingerprint image of each finger one by one, needs the same number of matching times same as the number of all file cards in the files at the maximum. The above-mentioned fingerprint matching device determines identity of given pair of fingers by attempting to match minutiae (bifurcations and end-points of ridges) positions. Therefore, the number of times that arithmetic for judging identity of fingerprint images is performed counts (file card numbers in files).times.(average number of fingers subject to the judgment for identity of fingerprint images in matching the search card against one file card), requiring much computation time compared with a preselecting technology.
Conventionally, in order to decrease the computational complexity required for the above matching process, it has been proposed to employ a preselecting technology which previously reduces the number of file cards to be used for matching a search card based on a prescribed standard before performing the matching process. This type of preselecting technology generally detects the features of the general pattern of ridges (hereinafter referred to as the pattern level feature) from the fingerprint image of each finger between the file cards and the search card, compares the obtained features of the pattern level features, and selects the file cards having the features similar to those on the search card to compare with the search card.
To evaluate the preselecting performance of such a preselecting technology, it is necessary to consider two yardsticks, reliability and selectivity. Reliability means a probability of correct judgment as identical when the given search card and the file card are a match, i.e. they both show the fingerprint images collected from the same person. A value indicating reliability is desired to be high, and 100% is the best. Selectivity means a probability of misjudgment as identical when a non-match is given, i.e. images on the search and file cards are collected from different persons. A value indicating selectivity is desired to be lower and 0% is the best. When a value which indicates selectivity is low, it is expressed as "selectivity is high".
As the features of the pattern level feature to be used for preselecting, conventionally proposed various standards can be used. Examples of such classification standards are described in documents such as "Fingerprint Classification by Directional Distribution Patterns" (Osamu NAKAMURA et al., Collection of Paper by Institute of Electronics and Communication Engineers of Japan, Vol. J65-D, No. 10, pp. 1286-1293, October 1982), "An Algorithm for Classification of Fingerprints Based on the Core" (Shin' ichiro ITO et al., Collection of Paper by The Institute of Electronics, Information and Communication Engineers, D-II, Vol. J73-D-II, No. 10, pp. 1733-1741, October 1990), and "The Science of Fingerprints" (U.S. Department of Justice, Federal Bureau of Investigation). According to the above documents, the fingerprint images can be classified into whorls, left loops, right loops and arches (hereinafter referred to as the pattern level feature type) depending on the pattern level feature forms and the positional relation of singular points. And, "The Science of Fingerprints" describes a method of classifying by using a permutation of sub-pattern level information such as the number of ridges between the singular points of fingerprint patterns in addition to the pattern level feature type of each finger to perform more accurate preselecting.
The conventional fingerprint matching device, which utilizes the above pattern level feature type to preselect the file cards to be compared, prepares a list of the pattern level feature types corresponding to the fingerprint images of respective fingers and classifies the file cards. And, to check the tenprint cards, the list of the pattern level feature types obtained from the fingerprint images of respective fingers on the search card are compared with the one of the pattern level feature types on the file cards, and matching is performed on a group of the file cards which have the same list of pattern level feature types as that of the search card. FIG. 8 shows an example of configuration of the conventional tenprint card preselector for performing the above-described preselecting method.
In FIG. 8, a tenprint card preselector 100 receives the fingerprint images of file cards from an external image storage device 101, a fingerprint pattern level feature judging apparatus 105 judges the pattern level feature types of respective fingers on the respective file cards, classifies the file cards according to the list of the pattern level feature types of ten fingers, and a file card storage 103 stores the classified file cards. Then, upon receiving the fingerprint images shown on a search card from the external image storage device 101, a fingerprint pattern level feature judging apparatus 106 similarly judges the pattern level feature types of respective fingers on the search card, and determines as a preselecting result a group of the file cards which have the list of the pattern level feature types of ten fingers matching to that of the pattern level feature types of the judged search card.
But, the above conventional tenprint card preselector had disadvantages that the same lists of the pattern level feature types of ten fingers were often obtained and preselecting was not satisfactory.
And, when the file cards were classified according to the list of the pattern level feature types of ten fingers, classification was uniquely determined, so that ambiguity of the pattern level feature type could not be allowed, and the pattern level feature types might be misjudged. In other words, if a corresponding pattern level feature type could not be specified in connection with the fingerprint of a certain finger, a plurality of corresponding candidates were considered, and if such a situation took place on a plurality of fingers, preselecting could not be determined efficiently.
Besides, since the classification according to the list of the pattern level feature types of ten fingers is fixed, preselecting having desired characteristics and performance according to the tradeoff of the two yardsticks, reliability and selectivity, could not be achieved with flexibility.
And, in the case of classifying the file cards by utilizing the list using sub-pattern level information on sub-features such as the number of ridges between the singular points of fingerprint patterns in addition to the pattern level feature types of respective fingers, ambiguity of the pattern level feature types and sub-pattern level information could not be permitted. Therefore, there was a disadvantage that satisfactory preselecting could not be made.